Time-optimal path planning and control using neural networks and a genetic algorithm

This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimise command tracking errors. The results indicate that the extended Kohonen network controller is more efficient than the trajectory pre-shaping scheme reported in early literature. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimisation problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. The results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridisation between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated.